{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting prices with a single-asset regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the independent and target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alpha_vantage.timeseries import TimeSeries\n",
    "\n",
    "# Update your Alpha Vantage API key here...\n",
    "ALPHA_VANTAGE_API_KEY = 'PZ2ISG9CYY379KLI'\n",
    "\n",
    "ts = TimeSeries(key=ALPHA_VANTAGE_API_KEY, output_format='pandas')\n",
    "df_jpm, meta_data = ts.get_daily_adjusted(\n",
    "    symbol='JPM', outputsize='full')\n",
    "df_gs, meta_data = ts.get_daily_adjusted(\n",
    "    symbol='GS', outputsize='full')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_x = pd.DataFrame({'GS': df_gs['5. adjusted close']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "jpm_prices = df_jpm['5. adjusted close']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing the linear regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "class LinearRegressionModel(object):\n",
    "    def __init__(self):\n",
    "        self.df_result = pd.DataFrame(columns=['Actual', 'Predicted'])\n",
    "\n",
    "    def get_model(self):\n",
    "        return LinearRegression(fit_intercept=False)\n",
    "\n",
    "    def learn(self, df, ys, start_date, end_date, lookback_period=20):\n",
    "        model = self.get_model()\n",
    "\n",
    "        for date in df[start_date:end_date].index:\n",
    "            # Fit the model\n",
    "            x = self.get_prices_since(df, date, lookback_period)\n",
    "            y = self.get_prices_since(ys, date, lookback_period)\n",
    "            model.fit(x, y.ravel())\n",
    "\n",
    "            # Predict the current period\n",
    "            x_current = df.loc[date].values\n",
    "            [y_pred] = model.predict([x_current])\n",
    "\n",
    "            # Store predictions\n",
    "            new_index = pd.to_datetime(date, format='%Y-%m-%d')\n",
    "            y_actual = ys.loc[date]\n",
    "            self.df_result.loc[new_index] = [y_actual, y_pred]\n",
    "\n",
    "    def get_prices_since(self, df, date_since, lookback):\n",
    "        index = df.index.get_loc(date_since)\n",
    "        return df.iloc[index-lookback:index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_reg_model = LinearRegressionModel()\n",
    "linear_reg_model.learn(df_x, jpm_prices, start_date='2018', \n",
    "                       end_date='2019', lookback_period=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "linear_reg_model.df_result.plot(\n",
    "    title='JPM prediction by OLS', \n",
    "    style=['-', '--'], figsize=(12,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Risk metrics for measuring prediction performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean absolute error (MAE) as a risk metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean absolute error: 2.4581681193682257\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "actual = linear_reg_model.df_result['Actual']\n",
    "predicted = linear_reg_model.df_result['Predicted']\n",
    "\n",
    "mae = mean_absolute_error(actual, predicted)\n",
    "print('mean absolute error:', mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean squared error (MSE) as a risk metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean squared error: 12.156831789513527\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mse = mean_squared_error(actual, predicted)\n",
    "print('mean squared error:', mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explained variance score as a risk metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained variance score: 0.533223710321685\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import explained_variance_score\n",
    "eva = explained_variance_score(actual, predicted)\n",
    "print('explained variance score:', eva)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R<sup>2</sup> as a risk metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score: 0.41668262740620565\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2 = r2_score(actual, predicted) \n",
    "print('r2 score:', r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "class RidgeRegressionModel(LinearRegressionModel):        \n",
    "    def get_model(self):\n",
    "        return Ridge(alpha=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_reg_model = RidgeRegressionModel()\n",
    "ridge_reg_model.learn(df_x, jpm_prices, start_date='2018', \n",
    "                      end_date='2019', lookback_period=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import (\n",
    "    accuracy_score, mean_absolute_error, \n",
    "    explained_variance_score, r2_score\n",
    ")\n",
    "def print_regression_metrics(df_result):\n",
    "    actual = list(df_result['Actual'])\n",
    "    predicted = list(df_result['Predicted'])\n",
    "    print('mean_absolute_error:', \n",
    "          mean_absolute_error(actual, predicted))\n",
    "    print('mean_squared_error:', mean_squared_error(actual, predicted))\n",
    "    print('explained_variance_score:', \n",
    "        explained_variance_score(actual, predicted))\n",
    "    print('r2_score:', r2_score(actual, predicted))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_absolute_error: 1.5894857982072854\n",
      "mean_squared_error: 4.519781391552921\n",
      "explained_variance_score: 0.7954234004448739\n",
      "r2_score: 0.7831287746949672\n"
     ]
    }
   ],
   "source": [
    "print_regression_metrics(ridge_reg_model.df_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting returns with a cross-asset momentum model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_spx, meta_data = ts.get_daily_adjusted(\n",
    "    symbol='SPX', outputsize='full')\n",
    "df_gld, meta_data = ts.get_daily_adjusted(\n",
    "    symbol='GLD', outputsize='full')\n",
    "df_dxy, dxy_meta_data = ts.get_daily_adjusted(\n",
    "    symbol='UUP', outputsize='full')\n",
    "df_ief, meta_data = ts.get_daily_adjusted(\n",
    "    symbol='IEF', outputsize='full')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_assets = pd.DataFrame({\n",
    "    'SPX': df_spx['5. adjusted close'],\n",
    "    'GLD': df_gld['5. adjusted close'],\n",
    "    'UUP': df_dxy['5. adjusted close'],\n",
    "    'IEF': df_ief['5. adjusted close'],\n",
    "}).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_assets_1m = df_assets.pct_change(periods=20)\n",
    "df_assets_1m.columns = ['%s_1m'%col for col in df_assets.columns]\n",
    "\n",
    "df_assets_3m = df_assets.pct_change(periods=60)\n",
    "df_assets_3m.columns = ['%s_3m'%col for col in df_assets.columns]\n",
    "\n",
    "df_assets_6m = df_assets.pct_change(periods=120)\n",
    "df_assets_6m.columns = ['%s_6m'%col for col in df_assets.columns]\n",
    "\n",
    "df_assets_12m = df_assets.pct_change(periods=240)\n",
    "df_assets_12m.columns = ['%s_12m'%col for col in df_assets.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lagged = df_assets_1m.join(df_assets_3m)\\\n",
    "    .join(df_assets_6m)\\\n",
    "    .join(df_assets_12m)\\\n",
    "    .dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2792 entries, 2008-02-12 to 2019-03-15\n",
      "Data columns (total 16 columns):\n",
      "SPX_1m     2792 non-null float64\n",
      "GLD_1m     2792 non-null float64\n",
      "UUP_1m     2792 non-null float64\n",
      "IEF_1m     2792 non-null float64\n",
      "SPX_3m     2792 non-null float64\n",
      "GLD_3m     2792 non-null float64\n",
      "UUP_3m     2792 non-null float64\n",
      "IEF_3m     2792 non-null float64\n",
      "SPX_6m     2792 non-null float64\n",
      "GLD_6m     2792 non-null float64\n",
      "UUP_6m     2792 non-null float64\n",
      "IEF_6m     2792 non-null float64\n",
      "SPX_12m    2792 non-null float64\n",
      "GLD_12m    2792 non-null float64\n",
      "UUP_12m    2792 non-null float64\n",
      "IEF_12m    2792 non-null float64\n",
      "dtypes: float64(16)\n",
      "memory usage: 359.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_lagged.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = jpm_prices.pct_change().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_linear_model = LinearRegressionModel()\n",
    "multi_linear_model.learn(df_lagged, y, start_date='2018', \n",
    "                         end_date='2019', lookback_period=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_linear_model.df_result.plot(\n",
    "    title='JPM actual versus predicted percentage returns',\n",
    "    style=['-', '--'], figsize=(12,8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_absolute_error: 0.019523019808983168\n",
      "mean_squared_error: 0.000722550861810258\n",
      "explained_variance_score: -2.7298040463350692\n",
      "r2_score: -2.738407558560419\n"
     ]
    }
   ],
   "source": [
    "print_regression_metrics(multi_linear_model.df_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An ensemble of decision trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "class BaggingRegressorModel(LinearRegressionModel):\n",
    "    def get_model(self):\n",
    "        return BaggingRegressor(n_estimators=20, random_state=0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "bagging = BaggingRegressorModel()\n",
    "bagging.learn(df_lagged, y, start_date='2018', \n",
    "              end_date='2019', lookback_period=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_absolute_error: 0.011469926472278664\n",
      "mean_squared_error: 0.0002463521857419121\n",
      "explained_variance_score: -0.2722603048485479\n",
      "r2_score: -0.2746021379561929\n"
     ]
    }
   ],
   "source": [
    "print_regression_metrics(bagging.df_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting trends with classification-based machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "y_direction = y >= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1998-01-05     True\n",
       "1998-01-06    False\n",
       "1998-01-07     True\n",
       "Name: 5. adjusted close, dtype: bool"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_direction.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "flags = list(y_direction.unique())\n",
    "flags.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False, True]\n"
     ]
    }
   ],
   "source": [
    "print(flags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the dataset of multiple assets as input variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_input = df_assets_1m.join(df_assets_3m).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2972 entries, 2007-05-25 to 2019-03-15\n",
      "Data columns (total 8 columns):\n",
      "SPX_1m    2972 non-null float64\n",
      "GLD_1m    2972 non-null float64\n",
      "UUP_1m    2972 non-null float64\n",
      "IEF_1m    2972 non-null float64\n",
      "SPX_3m    2972 non-null float64\n",
      "GLD_3m    2972 non-null float64\n",
      "UUP_3m    2972 non-null float64\n",
      "IEF_3m    2972 non-null float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 197.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_input.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "class LogisticRegressionModel(LinearRegressionModel):\n",
    "    def get_model(self):\n",
    "        return LogisticRegression(solver='lbfgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_reg_model = LogisticRegressionModel()\n",
    "logistic_reg_model.learn(df_input, y_direction, start_date='2018', \n",
    "                         end_date='2019', lookback_period=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Actual Predicted\n",
       "2018-01-02   True      True\n",
       "2018-01-03   True      True\n",
       "2018-01-04   True      True\n",
       "2018-01-05  False      True\n",
       "2018-01-08   True      True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_reg_model.df_result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Risk metrics for measuring classification-based predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "df_result = logistic_reg_model.df_result    \n",
    "actual = list(df_result['Actual'])\n",
    "predicted = list(df_result['Predicted'])\n",
    "\n",
    "matrix = confusion_matrix(actual, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[60 66]\n",
      " [55 70]]\n"
     ]
    }
   ],
   "source": [
    "print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.subplots(figsize=(12,8))\n",
    "sns.heatmap(matrix.T, square=True, annot=True, fmt='d', cbar=False, \n",
    "            xticklabels=flags, yticklabels=flags)\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.title('JPM percentage returns 2018');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy_score: 0.5179282868525896\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('accuracy_score:', accuracy_score(actual, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision_score: 0.5147058823529411\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "print('precision_score:', precision_score(actual, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recall score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recall_score: 0.56\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "print('recall_score:', recall_score(actual, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F1 Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score: 0.5363984674329502\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print('f1_score:', f1_score(actual, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "class SVCModel(LogisticRegressionModel):\n",
    "    def get_model(self):\n",
    "        return SVC(C=1000, gamma='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_model = SVCModel()\n",
    "svc_model.learn(df_input, y_direction, start_date='2018', \n",
    "                end_date='2019', lookback_period=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy_score: 0.5577689243027888\n",
      "precision_score: 0.5538461538461539\n",
      "recall_score: 0.576\n",
      "f1_score: 0.5647058823529412\n"
     ]
    }
   ],
   "source": [
    "df_result = svc_model.df_result\n",
    "actual = list(df_result['Actual'])\n",
    "predicted = list(df_result['Predicted'])\n",
    "\n",
    "print('accuracy_score:', accuracy_score(actual, predicted))\n",
    "print('precision_score:', precision_score(actual, predicted))\n",
    "print('recall_score:', recall_score(actual, predicted))\n",
    "print('f1_score:', f1_score(actual, predicted))    "
   ]
  }
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